An optimal segmentation with deep learning based inception network model for intracranial hemorrhage diagnosis

Traumatic Brain Injury (TBI) leads to intracranial hemorrhages (ICH), which is a severe illness resulted in death if it is not properly diagnosed and treated in the earlier stage. Presently, computer tomography (CT) images are widely used by radiologists to identify and locate the regions of ICH. But it is a tedious task and mainly depends on the professional radiologists. This paper develops new deep learning (DL)-based ICH diagnosis and classification (DL-ICH) model using optimal image segmentation with Inception Network. The proposed DL-ICH model involves preprocessing, segmentation, feature extraction, and classification. Firstly, the input data undergo format conversion where the NIfTI files are converted into JPEG format. Next, Kapur's thresholding with an elephant herd optimization (EHO) algorithm called KT-EHO is employed for image segmentation. Then, DL based Inception v4 network is applied as a feature extractor to extract a useful set of features and a multilayer perceptron (MLP) is used for classification. An extensive set of simulations takes place to ensure the effective diagnostic performance of the DL-ICH model and the results are investigated under diverse dimensions. The experimental results achieved better accuracy rate.